The present invention relates to adaptive interference cancelling and more particularly to adaptive interference cancelling through use of a pre-look cycle to determine the interference present during an inactive period of a primary signal during which a component of interest is not present and cancelling that interference during a subsequent active period of the primary signal during which the component of interest is present in the primary signal.
Removal of correlated interference signals by adaptive techniques can improve the operation of a number of different types of electronic systems such as radio frequency (RF) antennas, medical electronics, pattern recognition systems and so forth. The correlated interference signals may be noise or a combination of noise and other signals which are not desired. In these techniques, there is a source of a primary signal which has active and inactive periods and which includes a component of interest which is only present during the active periods. The signal provided by this source of the primary signal is contaminated by a number (N) of auxiliary signals. There are also sources from which those N auxiliary signals may be obtained.
Where the component of interest is only intermittently present in the primary signal, the signals from all N+1 sources (N auxiliary signals and the primary signal) during a pre-look interval, during which the component of interest is absent, are processed to determine the N weights each of which should multiply a different one of the N auxiliary signals during an active period when the component of interest is present in the primary signal. During the presence of the component of interest, each of the auxiliary signals is multiplied by its corresponding weight and these weighted signals are combined with the received primary signal. It is conventional to assign the primary signal a weight of one. The signs of the weights and their magnitudes are selected such that the resulting combination is the desired signal substantially free of correlated interference. Where there are N auxiliary signals the process of determining these N weights involves determining N(N+1)/2 correlation coefficients L.sub.ij. These correlation coefficients L.sub.ij are the elements of an N+1 by N+1 matrix which occupy the portion of the matrix below the diagonal of that matrix. The diagonal of this matrix is all ones and the elements above the diagonal are all zeroes. Prior art digital implementations of this type of system require N(N+3)/2 processors to determine the N(N+1)/2 correlation coefficients L.sub.ij. Thus, if there are twelve auxiliary signals, ninety processors are required. Twelve auxiliary signals is a realistic situation with respect to the cancellation of sidelobe signals in an adaptive RF antenna. In order to determine the weights, the N+1 by N+1 matrix of which the correlation coefficients are elements must be inverted. This matrix inversion process is computationally demanding and constitutes a bottleneck which severely limits the speed with which the weights can be determined. This matrix inversion process requires separate hardware from the N(N+3)/2 processors which determine the correlation coefficients.
Once the weights have been determined, then during an active period where the component of interest is present in the primary signal, each of the received N+1 signals is multiplied by its corresponding weight which was determined during the pre-look interval and those products are summed. The resulting sum is the desired signal as received, except for the cancellation of substantially all of the correlated interference present in the auxiliary signals.
Such interference cancellation can be vital in a number of different systems. However, at present the quantity of hardware and the time required to obtain this cancellation makes the use of such cancellation techniques in real time prohibitive in many of those systems. Thus, in order to make real-time systems of this type feasible there is a need for an adaptive interference cancellation system which requires fewer processors and requires less time to derive the weights than these prior art systems do.